TY - JOUR
T1 - Guest Editorial
T2 - Special Section on Data Analytics and Machine Learning for Network and Service Management-Part i
AU - Zincir-Heywood, Nur
AU - Casale, Giuliano
AU - Carrera, David
AU - Chen, Lydia Y.
AU - Dhamdhere, Amogh
AU - Inoue, Takeru
AU - Lutfiyya, Hanan
AU - Samak, Taghrid
PY - 2020
Y1 - 2020
N2 - Network and Service analytics can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, analytics and machine learning have found ways to improve reliability, configuration, performance, fault and security management. In particular, we see a growing trend towards using machine learning, artificial intelligence and data analytics to improve operations and management of information technology services, systems and networks.
AB - Network and Service analytics can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, analytics and machine learning have found ways to improve reliability, configuration, performance, fault and security management. In particular, we see a growing trend towards using machine learning, artificial intelligence and data analytics to improve operations and management of information technology services, systems and networks.
UR - http://www.scopus.com/inward/record.url?scp=85097741054&partnerID=8YFLogxK
U2 - 10.1109/TNSM.2020.3038736
DO - 10.1109/TNSM.2020.3038736
M3 - Review article
AN - SCOPUS:85097741054
SN - 1932-4537
VL - 17
SP - 1971
EP - 1974
JO - IEEE Transactions on Network and Service Management
JF - IEEE Transactions on Network and Service Management
IS - 4
M1 - 9289025
ER -